1. Field of the Invention
The present invention relates to a monitoring and optimization service. More particularly, the present invention relates to systems and methods for automated monitoring of consumer behaviors, data, and other factors that contribute to a credit score and controlling those factors to optimize credit scores.
2. Background of the Invention
Credit scores are not only an important aspect of modern life, but also one of the most important sources for financial wealth and loss, and its resulting impact on the quality of life. Practically every major financed purchase in modern society involves the determination of the purchaser's credit score before financing is approved. For example, the difference between a “good” credit score and an “average” credit score can be associated with literally thousands of dollars in excess interest expenses, fees, and even insurance premiums.
Despite its criticality in determining the creditworthiness of a consumer, the calculation of credit scores is a virtual black box for even sophisticated consumers due to the countless variables that are included in conventional credit score algorithms, the constant updating of these variables, and even changes to the algorithms themselves. These constraints leave consumers confused as to the answers to questions like, when is it the right time to close a credit card that is no longer needed, or should a certain behavior be changed in order to get a credit score increase?
Conventional systems lack the capability to analyze consumer behaviors on an ongoing and real-time basis and incorporate them into their recommendations for optimizing credit scores. In addition, existing systems lack the capability to update their data on a recurring basis and without user interaction, and to provide recommendations when the advantages for certain actions arise. In essence, existing systems only partially cater to consumers who are looking to simulate certain hypothetical scenarios at a certain point in time in the future given the modification of certain data which contribute to credit scores, and offer absolutely no value after the user stops actively interacting with the system.
Conventional systems also lack the ability to detect, on a real-time basis, consumer behaviors that need to be proactively communicated to them given the adverse effects that these behaviors can have on consumers' credit scores if they are not changed. For example, a consumer might have applied for a couple of credit cards in a short amount of time. Conventional systems do not have the ability to proactively identify this behavior and determine the effects of what will happen to the consumer's credit score if in the next 60 days he applies for one or more credit cards or loans, or the capability to alert the consumer that his credit score will suffer a significant decrease and therefore advise the consumer not to proceed with any such actions.
Thus, there is a need in the art for systems and methods, primarily used for monitoring and optimizing credit scores, which are easy to use, efficient, and capable of automatically and without user interaction provide feedback to the consumer as to which actions, which may be occurring in real time or could occur in the future, are beneficial or detrimental to the consumer's credit score.